1. Field of the Invention
The present invention is directed to a method for operating a magnetic resonance apparatus, particularly for the purpose of obtaining identically positioned slice images of a subject in temporally separated examinations.
2. Description of the Prior Art
Magnetic resonance technology is a known technique for acquiring images of the inside of the body of an examination subject. In magnetic resonance imaging, rapidly switched gradient fields that are generated by a gradient system are superimposed on a static, basic magnetic field in a magnetic resonance apparatus. The magnetic resonance apparatus also has a radio-frequency system that omits radio-frequency signals into the examination subject for triggering magnetic resonance signals, and that picks up the generated magnetic resonance signals. Image datasets and magnetic resonance images are produced on the basis of the image datasets.
In functional magnetic resonance imaging, a sense of image datasets is registered in a time sequence from the same area of an examination subject to be imaged. Appropriate methods are known for filtering out differences between the image datasets that are the result of a change in position of the region to be imaged with respect to the apparatus during the time sequence.
One group of methods for determining positional change from image data sets registered in chronological succession is based on a description of an arbitrary rigid body movement in three-dimensional space with six motion parameters, whereby three parameters identify translations and three parameters identify rotations. The parameters are represented, for example, in a column vector {right arrow over (q)}. The values of all voxels or selected voxels of a first image dataset and of a second, subsequently obtained image dataset, are respectively represented in a first column vector {right arrow over (x)} and in a second column vector {right arrow over (y)} in a coinciding sequence. The following equation, which is based on a Taylor expansion of the first order, is solved for determining a positional change between the registration times of the first and of the second dataset, i.e. for determining the motion factor, for example with an iterative method:                     y        →            -              x        →              =                                        [                          xe2x80x83                        ⁢                                                                                                      ∂                                              x                        1                                                                                    ∂                                              q                        1                                                                                                              ⋯                                                                                            ∂                                              x                        1                                                                                    ∂                                              q                        6                                                                                                                                          ⋮                                                  ⋰                                                  ⋮                                                                                                                        ∂                                              x                        n                                                                                    ∂                                              q                        1                                                                                                              ⋯                                                                                            ∂                                              x                        n                                                                                    ∂                                              q                        6                                                                                                                  ⁢                          xe2x80x83                        ]                    ·                      q            →                          ⁢                  xe2x80x83                ⁢        with        ⁢                  xe2x80x83                ⁢                  x          →                    =              [                                                            x                1                                                                        ⋮                                                                          x                n                                                    ]              ;      xe2x80x83    ⁢            y      →        =          [                                                  y              1                                                            ⋮                                                              y              n                                          ]        ;      xe2x80x83    ⁢            q      →        =          [                                                  q              1                                                            ⋮                                                              q              6                                          ]      
More detailed descriptions of acquisition algorithms for positional changes based on image datasets, are available in the book by R. S. J. Frackowiak et al, Human Brain Function, Academic Press, 1997, particularly chapter 3, pages 43 through 58, and the article by K. J. Friston et al. xe2x80x9cMovement-Related Effects in fMRI Time-Seriesxe2x80x9d Magnetic Resonance in Medicine 35 (1996), pages 346 through 355.
In another group of methods for acquisition of positional change based on image datasets, all points or specific, selected points of a first image dataset described in k-space, and of a second image dataset that has been produced following the first in terms of time, are compared to one another. The methods are based on the fact that, due to a change in position between the registration times of the two datasets, translations and/or rotations of the region to be imaged are represented by a modification of the phase and/or the magnitude of respective data points that are identically arranged within the two datasets. Embodiments of this type of method are described in greater detail in, for example, the article by L. C. Maas et al, xe2x80x9cDecoupled Automated Rotational and Translational Registration for Functional MRI Time Series Data: The DART Registration Algorithmxe2x80x9d, Magnetic Resonance in Medicine 37 (1997) pages 131 through 139, as well as in the article by Q. Chen et al., xe2x80x9cSymmetric Phase-Only Matched Filtering of Fourier-Mellin Transforms for Image Registration and Recognitionxe2x80x9d, IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 16, number 12 (1994), pages 1156 through 1168.
It is standard for maintaining sequence control calibration in patient treatment regimens which require multiple sessions, to repeatedly image the same region of an examination subject in successive examinations with a magnetic resonance apparatus that are spaced in time from one another. These examinations can ensue, for example, at a time spacing of a few hours or weeks. In an examination following a first examination, the operator of the magnetic resonance apparatus tries to position the examination subject in the apparatus and to set the apparatus with manual inputs so that the images to be registered correspond as closely as possible to those of the first examination with respect to positioning within the examination subject. Only a moderate coincidence can be achieved by such manual adjustment. Further, the degree of the coincidence is dependent on the respective operator. Moreover, such manual adjustment is comparatively time-consuming.
An object of the present invention is to provide a method for the operation of a magnetic resonance apparatus that, among other things, alleviates the aforementioned disadvantages associated with a sequence control calibration.
This object is inventively achieved in a method for the operation of a magnetic resonance apparatus wherein in a first examination of an examination subject, a first scout dataset of the examination subject is produced and with reference to which at least one first slice of the examination subject to be imaged is determined, and wherein a further scout dataset of the examination subject is produced in at least one further examination of the examination subject temporally following the first examination, and wherein a change in position between the first scout dataset and the further scout dataset is identified, and wherein at least one further slice of the examination subject to be imaged is defined according to the identified positional change, this at least one further slice exhibiting an identical positioning within the examination subject with respect to the first slice.
The inventive method allows the magnetic resonance apparatus to be set automatically, with high precision and in a time-efficient way in the further examination, so that the magnetic resonance images to be generated in the further examination exactly coincide with those of the first examination with respect to positioning within the examination subject. As a result, the ability to compare the magnetic resonance images is maximized. Differences between magnetic resonance images of the first examination and of the further examination are precluded, so that, for example, pathological changes can be clearly diagnosed as such.
In an embodiment, the above-described method for determining changes in position from image datasets on the basis of a first order Taylor expansion is utilized for the determination of the change in position.
In another embodiment, the scout datasets are generated as three-dimensional image datasets, particularly with a fast imaging technique, for example an echo planar method. As a result, arbitrary rotations and/or translations in the three-dimensional space can be determined from the scout datasets as changes in position. Further, the scout datasets can be registered in a time-efficient way.
In a further embodiment, the examination subject in the further examination is automatically positioned in the magnetic resonance apparatus in conformity with data stored for the first examination. In a first version, the examination subject in the further examination is thereby seated on a support mechanism in conformity with the first examination. This means that the patient is seated on his/her back and with the head in front in conformity with the first examination. Subsequently, a displacement of the support mechanism for positioning the region of the examination subject to be imaged in the imaging volume of the apparatus is automatically implemented on the basis of the stored data from the first examination and without an intervening pause, for example employing a laser sighting arrangement that marks the region to be imaged. In another version, the patient is arbitrarily seated on the support mechanism in the further examination within a predetermined position range. A camera system thereby acquires the contours of the patient dependent on the type of patient support. In conjunction with the stored data of the first examination, a displacement of the support mechanism is determined and implemented, so that the same region of the patient to be imaged is positioned in the imaging volume in the further examination as in the first examination.